Facial capture artificial intelligence for training models

Abstract

Methods and systems are provided for training a model using a simulated character for animating a facial expression of a game character. The method includes generating facial expressions of the simulated character using input label value files (iLVFs). The method includes capturing mesh data of the simulated character using a virtual camera to generate three-dimensional (3D) depth data of a face of the simulated character. In one embodiment, the 3D depth data being output as mesh files corresponding to frames captured by the virtual camera. The method includes processing the iLVFs and the mesh data to train the model. In one embodiment, the model is configured to receive input mesh files from a human actor to generate output label value files (oLVFs) that are used for animating the facial expression of the game character. In this way, a real human actor is not required for training the model.

Description

CLAIM OF PRIORITY[0001]This application claims priority under 35 U.S.C. 119(e) to U.S. Provisional Patent Application No. 63 / 170,334, filed Apr. 2, 2021, the disclosure of which is incorporated herein by reference in its entirety for all purposes.1. FIELD OF THE DISCLOSURE[0002]The present disclosure relates generally to animating facial expression of game characters, and more particularly to methods and systems for training a model using a simulated character for animating a facial expression of a game character.Background2. DESCRIPTION OF THE RELATED ART[0003]The video game industry has seen many changes over the years. In particular, technology related to facial animation in video games have become more sophisticated over the past several years resulting in game characters appearing more and more realistic. Today, game characters can express mood and emotions like a human face, which results in players feeling more immersed in the game world. To this end, developers have been see...

AI Technical Summary

Benefits of technology

The patent text describes a method for generating output label value files (oLVFs) for the facial expressions of a game character using a virtual cam and simulated character. This method eliminates the need for a real human actor to train the model, as the simulated character can capture thousands of facial expressions, actions, and poses. This saves time and money and allows for quick and efficient training of the model.

Problems solved by technology

Unfortunately, current facial animation processes are expensive, time consuming, and involves precise planning and directing.

Current facial animation process may be extremely time consuming and expensive.

Unfortunately, this process is extremely time-consuming and expensive.

As a result, the current process of producing facial animation for game characters can be inefficient which may not be effective in achieving high quality results under tight schedules.

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